Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

Abstract

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth’s ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1°×1° and 0.25°×0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 higher than 0.56, RMSE less than 0.1cm3cm−3, and Bias less than 0.07cm3cm−3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP.